Exploratory Plots for 2017-2018 Acoustic/Fish Data

Purpose To explore the Acoustic data gathered in 2017 and 2018 to expose important trends between sites, diurnal patterns, fish abundance, lunar phase, and coral reef acoustics.

Time-Series Acoustics

Acoustics Breakdown All acoustic metrics (SPL and ACI) are broken down into 3 frequency bands: Broadband (All frequencies), High Frequency (Frequencies between 1 kHz - 22 kHz), and Mid Frequency (Frequencies between 160 Hz and 1 kHz)

Note 2017 had a 10 minute duty cycle with 5 minutes recording while 2018 had a 15 minute duty cycle with 5 minutes recording, so the number of files averages differs between years

Frequency-Band Breakdowns

High Frequency

Plots of high frequency patterns, notice diurnal patterns with highest SPL at night and lowest during the day (this is shown in the literature), also notice the clear splits by site.

Notice, site 35 seems to have switched position between 2017 and 2018 but all of the other sites seem to be staying more or less in the same spot

  • I wonder if this has to do with macroalgal cover changes between 2017 and 2018

Mid Frequency

Plots of mid frequency patterns, notice opposite diurnal patterns with highest SPL during the day and lowest at night, also notice the clear splits by site.

Also, notice that 35 does a similar switch in Mid-frequency, going from the bottom in 2017 to the top in 2018

Combo Plots

Validations

Combined Model All variables are matched to the files that were used for Fish call counts (3:00, 9:00, 15:00, 21:00)

Box Plots

Confidence Intervals

##Distributions

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

Scatterplots

Scatterplots SPL

Plotting explanatory (Knocks, Calls, Herbivory, Snaps) against response variables (MF and HF) SPL

Scatterplots ACI

Plotting explanatory (Knocks, Calls, Herbivory, Snaps) against response variables (MF and HF) ACI

Patterns Breakdown SPL and ACI

Patterns in Tot_Knocks and Hour at Site and Hour Level - SPL

Breaking down the relationship between total knocks and MF to the site and hour level

Patterns in HF and Snaps at Site Level - SPL

Patterns in Tot_Knocks and Hour at Site and Hour Level - ACI

Regressions

Biotic Regressions - SPL

Running basic regressions linking the explanatory to the response at their lowest levels and combined to see how different sites/ hours change the regression - SPL 

## [1] 0.07154203

## [1] 0.07154203

## [1] 0.02502387

## [1] 0.1885504

## [1] 0.0001189407

## [1] 0.1562949

## [1] 0.01167862

## [1] 0.016759

## [1] 0.1426095

## [1] 0.01083477

## [1] 0.5760618

## [1] 0.007021195

## [1] 0.004048019

Abiotic Regressions (Wind) -SPL

Running basic regressions linking the wind to SPL at both HF and MF to see if wind speed is significantly affecting the sound

## Warning: Removed 1518 rows containing non-finite values (stat_smooth).
## Warning: Removed 1518 rows containing missing values (geom_point).

## Warning: Removed 1520 rows containing non-finite values (stat_smooth).
## Warning: Removed 1520 rows containing missing values (geom_point).

###Biotic Regressions - ACI Running basic regressions linking the explanatory to the response at their lowest levels and combined to see how different sites/ hours change the regression - ACI 

## [1] 7.59096e-05

## [1] 0.07197858

## [1] 0.0002458592

## [1] 2.285974e-05

## [1] 0.0456215

## [1] 0.03102541

## [1] 0.04651069

## [1] 0.03330172

## [1] 0.07934382

Models

Preliminary Models Looking into the relationships between biogenic sounds (Knocks/Calls and Snaps) and their frequency spectra (MF SPL/HF SPL) respectively.

Model 1

Looking at Total Knocks only SPL MF ~ Tot_Knocks

#model 1 looking at Total Knocks only
gfit1 <- glm(SPL_Midrange ~ Tot_Knocks, data = AC.DF1, family = Gamma)

summary(gfit1)
## 
## Call:
## glm(formula = SPL_Midrange ~ Tot_Knocks, family = Gamma, data = AC.DF1)
## 
## Deviance Residuals: 
##       Min         1Q     Median         3Q        Max  
## -0.068872  -0.021698  -0.008332   0.015069   0.171982  
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  9.553e-03  3.461e-05 276.019  < 2e-16 ***
## Tot_Knocks  -1.534e-06  3.914e-07  -3.918 0.000123 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 0.001098715)
## 
##     Null deviance: 0.22855  on 199  degrees of freedom
## Residual deviance: 0.21186  on 198  degrees of freedom
## AIC: 1068.1
## 
## Number of Fisher Scoring iterations: 3
par(mfrow = c(2,2))
plot(gfit1)

summary.glm(gfit1)$coefficients
##                  Estimate   Std. Error    t value      Pr(>|t|)
## (Intercept)  9.553059e-03 3.461011e-05 276.019343 5.079866e-258
## Tot_Knocks  -1.533644e-06 3.913970e-07  -3.918385  1.227169e-04

Model 2

Looking at Total Knocks and Number of Long Calls SPL MF ~ Tot_Knocks + Num_L_Calls

#model 1 looking at Total Knocks only
gfit2 <- glm(SPL_Midrange ~ Tot_Knocks + Num_L_calls, data = AC.DF1, family = Gamma)

summary(gfit2)
## 
## Call:
## glm(formula = SPL_Midrange ~ Tot_Knocks + Num_L_calls, family = Gamma, 
##     data = AC.DF1)
## 
## Deviance Residuals: 
##       Min         1Q     Median         3Q        Max  
## -0.068874  -0.021712  -0.008334   0.015083   0.171968  
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  9.553e-03  4.005e-05 238.518  < 2e-16 ***
## Tot_Knocks  -1.533e-06  3.946e-07  -3.886 0.000139 ***
## Num_L_calls  2.677e-08  3.231e-06   0.008 0.993399    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 0.001104281)
## 
##     Null deviance: 0.22855  on 199  degrees of freedom
## Residual deviance: 0.21186  on 197  degrees of freedom
## AIC: 1070.1
## 
## Number of Fisher Scoring iterations: 3
par(mfrow = c(2,2))
plot(gfit2)

summary.glm(gfit2)$coefficients
##                  Estimate   Std. Error       t value      Pr(>|t|)
## (Intercept)  9.552893e-03 4.005097e-05 238.518388890 1.738362e-244
## Tot_Knocks  -1.533301e-06 3.945605e-07  -3.886099544  1.391033e-04
## Num_L_calls  2.676589e-08 3.230984e-06   0.008284128  9.933987e-01

Model 3

Looking at Total Knocks/Number of long calls/Herbivory SPL MF ~ Tot_Knocks + Num_L_Calls + Num_Herbivory

#model 1 looking at Total Knocks only
gfit3 <- glm(SPL_Midrange ~ Tot_Knocks + Num_L_calls + Num_Herbivory, data = AC.DF1, family = Gamma)

summary(gfit3)
## 
## Call:
## glm(formula = SPL_Midrange ~ Tot_Knocks + Num_L_calls + Num_Herbivory, 
##     family = Gamma, data = AC.DF1)
## 
## Deviance Residuals: 
##       Min         1Q     Median         3Q        Max  
## -0.067662  -0.021756  -0.007807   0.015801   0.173266  
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)    
## (Intercept)    9.565e-03  4.063e-05 235.409  < 2e-16 ***
## Tot_Knocks    -1.539e-06  3.932e-07  -3.915 0.000125 ***
## Num_L_calls    3.309e-07  3.225e-06   0.103 0.918400    
## Num_Herbivory -3.975e-06  2.555e-06  -1.556 0.121409    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 0.00109656)
## 
##     Null deviance: 0.22855  on 199  degrees of freedom
## Residual deviance: 0.20923  on 196  degrees of freedom
## AIC: 1069.6
## 
## Number of Fisher Scoring iterations: 3
par(mfrow = c(2,2))
plot(gfit3)

summary.glm(gfit3)$coefficients
##                    Estimate   Std. Error     t value      Pr(>|t|)
## (Intercept)    9.564541e-03 4.062942e-05 235.4092305 2.341236e-242
## Tot_Knocks    -1.539367e-06 3.932317e-07  -3.9146563  1.248754e-04
## Num_L_calls    3.308596e-07 3.225337e-06   0.1025814  9.184001e-01
## Num_Herbivory -3.975108e-06 2.555300e-06  -1.5556324  1.214087e-01

Model 4 - HF SPL and Snaps

Looking at Snaps and their effect on the HF SPL SPL HF ~ Snaps Distributions look normal so this is a linear model

fit4 <- lm(SPL_HF ~ Snaps, data = AC.DF1)
summary(fit4)
## 
## Call:
## lm(formula = SPL_HF ~ Snaps, data = AC.DF1)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -7.4772 -2.6200 -0.4764  2.6614  8.3553 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 91.690030   5.741405  15.970  < 2e-16 ***
## Snaps        0.017654   0.003924   4.499 1.16e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 3.549 on 198 degrees of freedom
## Multiple R-squared:  0.09275,    Adjusted R-squared:  0.08817 
## F-statistic: 20.24 on 1 and 198 DF,  p-value: 1.162e-05
par(mfrow = c(2,2))
plot(fit4)

summary(fit4)$coefficients
##                Estimate  Std. Error   t value     Pr(>|t|)
## (Intercept) 91.69002981 5.741405123 15.969963 1.943186e-37
## Snaps        0.01765414 0.003923967  4.499054 1.162338e-05